Activities such as searching for a favorite song or frequently changing radio stations distract a driver's attention and can cause accidents. For this and other reasons, systems for recommending music to drivers have become popular in the market. However, the existing music recommendation systems have numerous deficiencies and do not accurately recommend music that drivers want to listen to.
A first deficiency in existing recommendation systems is that they do not account for how a user's music preferences change depending on the environment in which the user listens to the music. These existing recommendation systems make music recommendations based merely on a user's static preferences regarding a set of music attributes (e.g., artist, genre, album, lyric type, instrument use, tempo, etc.). As a result, these systems recommend the same music over, and over again to the driver without considering other factors that affect which music the driver wants to listen to. Existing systems have no ability to consider external factors that influence a user's music preferences such as the driver's physical state, the driver's psychological state, the type of road the driver is driving on, the surrounding traffic, the time of day, the driver's routines, etc.
A second deficiency in existing recommendation systems is that they are not able to account for the fact that the music preference of the driver changes over time. These systems use models to generate their music recommendations, but these models do not change over time. Instead, they are static. As a result, the existing systems do not refine music recommendations by accumulating more and more data describing user's music preferences.
A third deficiency in existing recommendation systems is that they are not able to consider the affect of environmental factors such as the weather on a driver's music preferences. As a result, these existing recommendation systems are unable to improve the recommendation models based on environmental factors.
A fourth problem present with these systems is that they are limited to making only music recommendations. However, some users desire recommendations for other content such as news programs, podcasts, audio books, movies, television programs, etc.